System and method for controlling heat dissipation through service level agreement analysis by modifying scheduled processing jobs

ABSTRACT

The system and method generally relate to reducing heat dissipated within a data center, and more particularly, to a system and method for reducing heat dissipated within a data center through service level agreement analysis, and resultant reprioritization of jobs to maximize energy efficiency. A computer implemented method includes performing a service level agreement (SLA) analysis for one or more currently processing or scheduled processing jobs of a data center using a processor of a computer device. Additionally, the method includes identifying one or more candidate processing jobs for a schedule modification from amongst the one or more currently processing or scheduled processing jobs using the processor of the computer device. Further, the method includes performing the schedule modification for at least one of the one or more candidate processing jobs using the processor of the computer device.

FIELD OF THE INVENTION

The present invention generally relates to reducing heat dissipatedwithin a data center, and more particularly, to a system and method forreducing heat dissipated within a data center through service levelagreement analysis, and resultant reprioritization of jobs to maximizeenergy efficiency.

BACKGROUND

A traditional data center may conventionally include a plurality ofindividual computing resources in one open area having, e.g., fourwalls. A data center (or other physical space) beneficially has, wherepossible, an optimized heating and cooling infrastructure. Maintainingdata centers at desired temperatures (e.g., set points) helps preventcomputer hardware (e.g., information technology (IT) infrastructure)from overheating and malfunctioning. To this end, many data centers arecooled to relatively low temperatures (e.g., 65° F.) to increaseequipment reliability and useful life, and to avoid downtime for repairand/or replacement.

Conventional cooling systems cool the entire data center to atemperature well below the set point so that IT equipment operating inthe hot spots does not exceed the set point. In other words, existingcooling systems resort to a sort of ‘overkill’ by cooling the entirevolume of the data center to well below the set point, which increasesoperational costs and wastes energy. Moreover, with the increasingawareness and desire to operate in a “green” manner, such excessive useof energy is undesirable.

A service level agreement (frequently abbreviated as SLA) is a part of aservice contract where the level of service, e.g., for performing aprocess, is formally defined. In practice, the term SLA is sometimesused to refer to the contracted delivery time (of the service) orperformance. For example, an SLA may be a negotiated agreement betweentwo parties where one is the customer and the other is the serviceprovider. The SLA can be a legally binding formal or informal“contract.”

More specifically, the SLA may record a common understanding about, forexample, services, priorities, responsibilities, guarantees andwarranties. Each area of service scope may have the “level of service”defined. The SLA may specify the levels of availability, serviceability,performance, operation, or other attributes of the service such asbilling. The “level of service” can also be specified as “target” and“minimum,” which allows customers to be informed as to what to expect(the minimum), whilst providing a measurable (average) target value thatshows the level of organization performance. In some contracts,penalties may be agreed in the case of non compliance of the SLA. The“agreement” relates to the services the customer receives, and not howthe service provider delivers that service.

A data center may begin to process jobs, for example, once they arereceived by the data center and/or according to a scheduler. That is,for example, a data center may receive a number of processing jobs,e.g., three processing jobs. Moreover, the data center may beginprocessing these jobs upon receiving them.

However, while some of these jobs may need to be, for example, startedright away and/or run at maximum capacity, in order to meet those jobs'SLAs, other jobs may have more time, while remaining in compliance withtheir respective SLAs, to complete the processing. That is, continuingwith the above example, the SLAs for the first job may indicate thatthis job should be started right away and/or run at maximum capacity, inorder to meet the SLA. However, the SLAs for the second and third jobsmay indicate that these jobs may not need to be started right awayand/or run at maximum capacity in order to maintain compliance withtheir respective SLAs.

Accordingly, there exists a need in the art to overcome the deficienciesand limitations described hereinabove.

SUMMARY

In a first aspect of the invention, a computer implemented methodincludes performing a service level agreement (SLA) analysis for one ormore currently processing or scheduled processing jobs using a processorof a computer device. Additionally, the method comprises identifying oneor more candidate processing jobs for a schedule modification fromamongst the one or more currently processing or scheduled processingjobs using the processor of the computer device. Further, the methodcomprises performing the schedule modification for at least one of theone or more candidate processing jobs using the processor of thecomputer device.

In another aspect of the invention, a system comprises a service levelagreement (SLA) analysis tool operable to perform a service levelagreement (SLA) analysis for one or more currently processing orscheduled processing jobs of a data center, and identify one or morecandidate processing jobs for a schedule modification from amongst theone or more currently processing or scheduled processing jobs.Additionally, the system comprises a scheduling tool operable to performthe schedule modification for at least one of the one or more candidateprocessing jobs.

In an additional aspect of the invention, a computer program productcomprising a computer usable storage medium having readable program codeembodied in the medium is provided. The computer program productincludes at least one component operable to perform a service levelagreement (SLA) analysis for one or more currently processing orscheduled processing jobs of a data center using a processor of acomputer device to determine one or more processing jobs, from the oneor more currently processing or scheduled processing jobs, which can besubject to a schedule modification without incurring an SLA violation.Additionally, the at least one component operable to identify one ormore candidate processing jobs for the schedule modification fromamongst the one or more currently processing or scheduled processingjobs and perform the schedule modification for at least one of the oneor more candidate processing jobs. The schedule modification comprisesat least one of: a delay of the one or more candidate processing jobs; aslowing of the one or more candidate processing jobs; and a relocationof the one or more candidate processing jobs.

In a further aspect of the invention, a computer system for controllingheat dissipation through a service level agreement (SLA) analysiscomprises a CPU, a computer readable memory and a computer readablestorage media. Additionally, the computer system comprises first programinstructions to perform the service level agreement (SLA) analysis forone or more currently processing or scheduled processing jobs of a datacenter. Furthermore, the computer system comprises second programinstructions to identify one or more candidate processing jobs for aschedule modification from amongst the one or more currently processingor scheduled processing jobs. Moreover, the computer system comprisesthird program instructions to perform the schedule modification for atleast one of the one or more candidate processing jobs. The first,second and third program instructions are stored on the computerreadable storage media for execution by the CPU via the computerreadable memory.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present invention is described in the detailed description whichfollows, in reference to the noted plurality of drawings by way ofnon-limiting examples of exemplary embodiments of the present invention.

FIG. 1 shows an illustrative environment for implementing the steps inaccordance with aspects of the invention;

FIG. 2 shows an exemplary depiction of an application job scheduledwithin the data center processed at maximum capacity;

FIG. 3 shows an exemplary depiction of an application job scheduledwithin the data center processed at throttled capacity in accordancewith aspects of the invention; and

FIGS. 4 and 5 show exemplary flows in accordance with aspects of theinvention.

DETAILED DESCRIPTION

The present invention generally relates to reducing heat dissipatedwithin a data center, and more particularly, to a system and method forreducing heat dissipated within a data center through service levelagreement analysis, and resultant reprioritization of jobs to maximizeenergy efficiency. By analyzing the service level agreements andapplication job schedules, the application workload of a data center maybe scaled back (e.g., by not running the job at maximum speed) to reducethe heat and energy output within a data center while still meeting aSLA. For example, application jobs that have a lower priority (asindicated by the SLA) may be delayed for execution during known periodsof maximum workload, when overall heat output may be a concern.

By implementing the present invention, unevenness (or burstiness) ofjobs may be reduced so that periods of intense heat in the data center(which would require additional cooling, and thus, additional energyexpenditure) are not unnecessarily produced. To reduce such burstiness,for example, low-priority jobs (which will increase the data centercooling requirements) may be run only when other jobs are not running(when the data center is cooler). In embodiments, this can include, forexample, throttling back processor speeds of the computing resourcesperforming the low-priority jobs or by delaying computations for thelow-priority jobs until expected lower-utilized times.

By delaying these lower priority jobs, the present invention is operableto maintain the data center temperature as close to constant aspossible. This smoothing of “heat loads” on the data center will resultin lower data center cooling requirements and thus, will result in lowercooling costs.

Implementing the present invention allows for a data center to betterutilize its resources by throttling the application workload managed byits service level agreement (SLA) target. An aim of the presentinvention is to conserve energy and computing power by recognizing thetime of the application job, the SLA target for the job and the currentenvironmental parameters, e.g., within the data center, so that lowerpriority jobs (e.g., as indicated by their respective SLAs) may, forexample, be throttled-back to limit their energy usage but still meettheir SLA targets.

By adhering to the SLA targets for the application workload, theapplication jobs within the data center may be, for example, delayed orotherwise throttled back to limit their energy usage and heat output.This may help reduce the heat output, control power consumption and heatdissipation within a data center by controlling the speed at which thejobs are processed. Using the SLAs as a guide, jobs may be delayed torun during times at which energy costs are lower (for example, at night)or when the environmental parameters of the data center are best suitedfor running their workload. By time-shifting, throttling and/orrelocating workload based upon other computational workloads within adata center, the present invention is operable to smooth the heatcharacteristics within a given data center. Additionally, throttlingback the processing power may reduce the costs to cool the data centerand lengthen the mean-time to failure for critical systems that areaffected by changes in heat, temperature and humidity.

System Environment

As will be appreciated by one skilled in the art, the present inventionmay be embodied as a system, method or computer program product.Accordingly, the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,the present invention may take the form of a computer program productembodied in any tangible medium of expression having computer-usableprogram code embodied in the medium.

Any combination of one or more computer usable or computer readablemedium(s) may be utilized. The computer-usable or computer-readablemedium may be, for example but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,device, or propagation medium. More specific examples (a non-exhaustivelist) of the computer-readable medium would include the following:

-   -   an electrical connection having one or more wires,    -   a portable computer diskette,    -   a hard disk,    -   a random access memory (RAM),    -   a read-only memory (ROM),    -   an erasable programmable read-only memory (EPROM or Flash        memory),    -   an optical fiber,    -   a portable compact disc read-only memory (CDROM),    -   an optical storage device,    -   a transmission media such as those supporting the Internet or an        intranet, and/or    -   a magnetic storage device.

The computer-usable or computer-readable medium could even be paper oranother suitable medium upon which the program is printed, as theprogram can be electronically captured, via, for instance, opticalscanning of the paper or other medium, then compiled, interpreted, orotherwise processed in a suitable manner, if necessary, and then storedin a computer memory.

In the context of this document, a computer-usable or computer-readablemedium may be any medium that can contain, store, communicate,propagate, or transport the program for use by or in connection with theinstruction execution system, apparatus, or device. The computer-usablemedium may include a propagated data signal with the computer-usableprogram code embodied therewith, either in baseband or as part of acarrier wave. The computer usable program code may be transmitted usingany appropriate medium, including but not limited to wireless, wireline,optical fiber cable, RF, etc.

Computer program code for carrying out operations of the presentinvention may be written in any combination of one or more programminglanguages, including an object oriented programming language such asJava, Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork. This may include, for example, a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

FIG. 1 shows an illustrative environment 10 for managing the processesin accordance with the invention. To this extent, the environment 10includes a server or other computing system 12 that can perform theprocesses described herein. In particular, the server 12 includes acomputing device 14. The computing device 14 can be resident on anetwork infrastructure or computing device of a third party serviceprovider (any of which is generally represented in FIG. 1).

The computing device 14 includes an historical analysis (HA) tool 30, anenvironmental analysis (EA) tool 35, a service level agreement (SLA)analysis tool 40, and a scheduling tool 45, which are operable todetermine data center historical conditions, determine data centerenvironmental conditions (including data center processing conditions),determine SLA requirements for processing jobs, and schedule processingjobs based on the output of the HA tool 30, the EA tool 35 and the SLAanalysis tool 40, e.g., the processes described herein. The HA tool 30,the EA tool 35, the SLA analysis tool 40 and the scheduling tool 45 canbe implemented as one or more program code in the program control 44stored in memory 22A, as separate or as combined single or multipurposehardware modules. For example, the HA tool 30, the EA tool 35 and/or theSLA analysis tool 40 can also be a separate hardware device, such as aserver, each having its own processor(s) as discussed herein. The HAtool 30, the EA tool 35 and/or the SLA analysis tool 40 can also beseparate computing devices associated with one or more serversrepresented by server 12 of FIG. 1.

The computing device 14 also includes a processor 20, memory 22A, an I/Ointerface 24, and a bus 26. The memory 22A can include local memoryemployed during actual execution of program code, bulk storage, andcache memories which provide temporary storage of at least some programcode in order to reduce the number of times code must be retrieved frombulk storage during execution. In addition, the computing deviceincludes random access memory (RAM), a read-only memory (ROM), and anoperating system (O/S).

The computing device 14 is in communication with the external I/Odevice/resource 28 and the storage system 22B. The I/O device 28, forexample, can comprise any device that enables an individual to interactwith the computing device 14 or any device that enables the computingdevice 14 to communicate with one or more other computing devices usingany type of communications link. The external I/O device/resource 28 maybe for example, a handheld device, PDA, handset, keyboard etc. Moreover,as shown in FIG. 1 and explained further below, the computing device 14is in communication with one or more environmental sensors 50.

In general, the processor 20 executes computer program code (e.g.,program control 44), which can be stored in the memory 22A and/orstorage system 22B. Moreover, in accordance with aspects of theinvention, the program control 44 (having program code) controls the HAtool 30, the EA tool 35, the SLA analysis tool 40 and the schedulingtool 45. While executing the computer program code, the processor 20 canread and/or write data to/from memory 22A, storage system 22B, and/orI/O interface 24. The program code executes the processes of theinvention. The bus 26 provides a communications link between each of thecomponents in the computing device 14.

The computing device 14 can comprise any general purpose computingarticle of manufacture capable of executing computer program codeinstalled thereon (e.g., a personal computer, server, etc.). However, itis understood that the computing device 14 is only representative ofvarious possible equivalent-computing devices that may perform theprocesses described herein. To this extent, in embodiments, thefunctionality provided by the computing device 14 can be implemented bya computing article of manufacture that includes any combination ofgeneral and/or specific purpose hardware and/or computer program code.In each embodiment, the program code and hardware can be created usingstandard programming and engineering techniques, respectively.

Similarly, the computing infrastructure 12 is only illustrative ofvarious types of computer infrastructures for implementing theinvention. For example, in embodiments, the server 12 comprises two ormore computing devices (e.g., a server cluster) that communicate overany type of communications link, such as a network, a shared memory, orthe like, to perform the process described herein. Further, whileperforming the processes described herein, one or more computing deviceson the server 12 can communicate with one or more other computingdevices external to the server 12 using any type of communications link.The communications link can comprise any combination of wired and/orwireless links; any combination of one or more types of networks (e.g.,the Internet, a wide area network, a local area network, a virtualprivate network, etc.); and/or utilize any combination of transmissiontechniques and protocols.

In embodiments, a service provider, such as a Solution Integrator, couldoffer to perform the processes described herein. In this case, theservice provider can create, maintain, deploy, support, etc., thecomputer infrastructure that performs the process steps of the inventionfor one or more customers. These customers may be, for example, anybusiness that uses technology. In return, the service provider canreceive payment from the customer(s) under a subscription and/or feeagreement and/or the service provider can receive payment from the saleof advertising content to one or more third parties.

Historical Analysis Tool

In embodiments, the historical analysis (HA) tool 30 is operable todetermine a historical analysis of the processing jobs in a data center.For example, with a reoccurring job, the HA tool 30 may determine howlong this job typically requires based on, for example, historical orother empirical data. Additionally, in embodiments, the HA tool 30 may,for example, determine a percentage of overall data center processingcapacity a job, e.g., a reoccurring job, may typically require. Forexample, the HA tool 30 may determine that a given transactionprocessing job typically only runs around forty percent of overall datacenter capacity, e.g., using a statistical analysis.

In embodiments, the HA tool 30 may store historical data center usage ina database, e.g., storage system 22B (as shown in FIG. 1). Additionally,the percentages of overall data center processing capacity may be storedin a database, e.g., storage system 22B. Moreover, the HA tool 30 mayaccess the stored historical data center usage information, for example,in order to determine how long a reoccurring job typically requires.Additionally, the HA tool 30 may access the stored historical datacenter usage information to estimate processing requirements for atleast one currently processing or scheduled processing job.

Environmental Analysis Tool

According to an aspect of the invention, the environmental analysis (EA)tool 35 may be used to perform an environmental analysis of a datacenter. An environmental analysis of a data center is performed todetermine, for example, the hot and cold zones and catalog the currentenvironmental factors (temperature by zone, humidity, analysis ofairflow, etc.). More specifically, a plurality of environmental sensors50 may be located throughout the data center. In embodiments, theplurality of environmental sensors 50 may include indoor temperaturesensors, outdoor temperature sensors, airflow sensors and humiditysensors, amongst other environmental sensors known to those of skill inthe art. Moreover, in embodiments, the environmental sensors 50 may be,for example, evenly spaced throughout the data center. In embodiments,the environmental sensors 50 may be located in known data center hotspots. For example, in embodiments, the environmental sensors 50 may belocated on the ceiling of the data center, the floor of the data centerand/or the walls of the data center (e.g., at differing elevations inthe walls of the data center).

The EA tool 35 may receive real-time environmental, e.g., temperatureand/or humidity, readings from the plurality of environmental sensors50. In embodiments, the EA tool 35 may determine average temperaturesfor regions of the data center based on the real-time temperaturereadings from the plurality of environmental sensors 50. Moreover, basedon the real-time temperature readings, the EA tool 35 can determine anenvironmental analysis of the data center. For example, the EA tool 35can determine regions of the data center that are very hot, e.g.,regions having a number of currently operating computer resources, andregions of the data center that are very cool, e.g., regions in which nocomputer resources are currently operating. The EA tool 35 can alsodetermine air flow paths, e.g., hot air flow paths, based on thereal-time temperature readings from the plurality of environmentalsensors 50. For example, the EA tool 35 can utilize the real-timetemperature readings to determine, for example, upon activation of acomputer resource, the flow path of hot air traveling from the computerresource in the data center.

The EA tool 35 may access a job schedule (e.g., stored in storage system22B), such that the EA tool 35 is aware of scheduled jobs for thedifferent computing resources of the data center. Furthermore, the EAtool 35 may determine current percentages of overall data centerprocessing capacity particular processing jobs are currently requiring,e.g., in real time. In embodiments, the EA tool 35 is also operable todetermine environmental conditions of other data centers. For example,the EA tool 35 may receive environmental conditions of other datacenters in order to determine whether one or more of the other datacenters would be suitable for a processing job relocation, as discussedfurther below.

Service Level Agreement Analysis Tool

According to further aspects of the invention, the service levelagreement (SLA) analysis tool 40 is operable to examine SLAs for the oneor more processing jobs, e.g., currently processing jobs and/or pendingjobs. SLAs for the data center processing jobs, e.g., currentlyprocessing jobs and/or pending jobs, may be stored in a database, e.g.,storage system 22B. The SLA analysis tool 40 is operable to access thedatabase to examine the SLAs and to determine those jobs (orapplications) that may, for example, be delayed, slowed and/or relocatedwhile still meeting their respective SLAs. For example, if anapplication has an SLA target to finish within two hours, but wouldlikely finish within one hour based on the current data center loadingand CPU clock speed (as determined from the HA tool 30 and/or the EAtool 35), then the SLA analysis tool 40 may determine that theapplication is a candidate for delay or slowing, e.g., CPU clock speedreduction. That is, continuing to execute the job may result in a shortterm temperature increase of the data center. Thus, as described furtherbelow, the infrastructure running the application job may be throttledback to complete the workload within the two hours, by slowing and/ordelaying processing on the underlying IT infrastructure. The slowingand/or delaying ensures that maximized energy efficiencies and reduced‘burstiness’ occurs during the job duration.

In other words, the present invention is operable to throttle backworkload for lower priority job workloads to maximize the energyefficiencies within a data center. As opposed to running the applicationworkload or job at full-speed and as fast as possible, as long as theSLAs are met, the present invention may throttle back the capacity tolower energy consumption while still maintaining the SLA targets for theapplication.

According to an aspect of the invention, the SLA analysis tool 40 isoperable to check the application job SLAs to determine a scheduledcompletion time of the job. In embodiments, the SLA analysis tool 40 maydetermine the application job SLAs before execution of the applicationjob. Additionally, in embodiments, the SLA analysis tool 40 is operableto determine the application job SLAs during execution, e.g., in realtime.

According to an exemplary embodiment, the SLA analysis tool 40 mayassign a numerical value, e.g., between 1-7 based on priority of theapplication's SLAs. Applications having a job priority value of, forexample, between 4-7 may be candidates to participate in the throttlingof workloads. For example, if an application has an SLA requiring jobcompletion in ten hours when the job historically takes nine hours(e.g., based on historical data, for example, as determined by the HAtool 30), the SLA analysis tool 40 may assign the application anumerical value (e.g., a throttle/delay numerical value) of 1,indicating that the application is not a candidate for the throttling ofworkloads. That is, with this example, there is not much excess time (9hours−8 hours=1 hour) for delaying or slowing of this application.

However, with another example, an application may have an SLA indicatinga required job completion in twenty-four hours when the job historicallytakes two hours (e.g., based on historical data, for example, asdetermined by the HA tool 30). The SLA analysis tool 40 may assign theapplication a numerical value of 6, indicating that the application is acandidate for the throttling of workloads. Additionally, the SLAanalysis tool 40 may rank the applications (e.g., currently processingand pending applications) according to their respective numericalvalue(s). Moreover, as discussed further below, in embodiments, thescheduling tool 45 may select those application candidates for, e.g.,delaying, slowing and/or relocating, based on their respective numericalvalue(s), e.g., with those applications having the highest rank (e.g., 7with the example set forth above) being selected for delaying, slowingand/or relocating prior to applications with lower numerical values.

In an exemplary non-limiting embodiment, the SLA analysis tool 35 maydetermine a numerical value for an application by determining the ratioof time required (as indicated by, for example, the HA tool 30) to thetime remaining (as indicated by the SLA). For example, with the firstabove example, the ratio is 9 hours/10 hours=0.90. With the second aboveexample, the ratio is 2 hours/24 hours=0.083. Furthermore, the SLAanalysis tool 40 may access a database, e.g., storage system 22B (asshown in FIG. 1) containing predetermined ranges of ratios with theirrespective corresponding numerical values. The predetermined ranges ofratios may be modified and/or updated by, for example, a user or serviceprovider, amongst others.

In a further embodiment, data centers located, for example, in differentlocations, climates and/or geographical regions, may have differentenergy costs at any given time. For example, if a data center is in ahot climate, e.g., on a particularly hot day, when energy demand, andthus, energy costs, are relatively high for that data center, coolingcosts for that data center may be lowered by relocating one or moreprocessing jobs to another data center. Thus, with a further aspect ofthe invention, the SLA analysis tool 40 may examine SLAs with regard towhere (e.g., at what data center or where in the world) the jobprocessing occurs. For example, a processing job application may have anSLA that requires that the processing job be performed at a particulardata center. Alternatively, another processing job may have an SLA thatmay, for example, specify a particular geographic region or may not haveany requirements with regard to the location of the job processing.Accordingly, the SLA analysis tool 40 is further operable to examineSLAs for any requirements with regard to where a particular processingjob occurs.

Based on the examination, the SLA analysis tool 40 is also able toassign a numerical value based on whether a particular job may beprocessed at a different data center, while still meeting the SLA forthat job processing application. Expanding on the immediately aboveexample, the SLA analysis tool 40 may assign the first processing job anumerical value (e.g., a relocation numerical value) of 1, as thisprocessing job must be performed at this particular data center, andthus is not a candidate for processing job relocation. Furthermore, theSLA analysis tool 40 may identify a processing job having an SLAindicating no job location constraints for the processing job. Thus, theSLA analysis tool 40 may assign this processing job a numerical value of7, indicating that this job is an excellent candidate for jobrelocation, while still maintaining the processing job's SLAs.

By reviewing the SLAs of applications, e.g., currently processing and/orscheduled to be processed by a data center, the SLA analysis tool 40 canidentify those applications (e.g., processing jobs) that, based on theirrespective SLAs, may be candidates for, e.g., delaying slowing and/orrelocation. In embodiments, the SLA analysis tool 40 may forward theidentified candidates to the scheduling tool 45, described furtherbelow.

Scheduling Tool

According to aspects of the invention, the scheduling tool 45 isoperable to maximize the energy efficiency of a data center by, forexample, throttling back the application job workload as adjusted by itsservice level targets. The scheduling tool 45 may throttle back theapplication job workload for a data center by, for example, delaying,slowing and/or relocating one or more currently processing and/orscheduled jobs for the data center.

The scheduling tool 45 may determine whether to delay those lowerpriority jobs (e.g., as identified as the SLA analysis tool 45) untilhigh priority jobs have finished. Reducing concurrent data centerprocessing will result in lower heat output of the data center, andconsequently lower cooling requirements and costs.

Additionally, the scheduling tool 45 may determine whether to slow oneor more jobs which has been identified as a candidate for slowing by theSLA analysis tool 40 (e.g., will complete ahead of schedule may beslowed by, e.g., reducing CPU clock speed, while the SLA associated withthe lower priority job is not violated). Long running jobs that may bedelayed without affecting an SLA may, for example, be slowed, orthrottled back, through the use of slowing CPU clock speeds to reducethe energy requirements for the application workload. By the schedulingtool 45 reducing clock speeds of those jobs that will complete ahead ofschedule (e.g., based on the HA tool 30), lower heat output of the datacenter will result. For example, instead of running a job at maximumcapacity for a short duration, the scheduling tool 45 may dictate thatthe application job may be extended for a longer period of time at alower capacity in order to reduce the heat output and energy usage.

Furthermore, the scheduling tool 45 may determine whether to relocatejobs (e.g., determined by the SLA analysis tool 40 as candidates forrelocation), e.g., to another region of the data center or another datacenter, in order to reduce the heat output and energy usage. Inembodiments, the scheduling tool 45 may also utilize more efficientservers in the process or route workload to systems in a cooler part ofthe data center. Reducing concurrent data center processing, excessiveprocessing on servers in a hotter region of a data center and/orprocessing on inefficient servers, for example, will result in lowerheat output of the data center, and consequently lower coolingrequirements and costs.

In embodiments, along with the application job priority, for example, inorder to select one or more applications for relocation, e.g., withinthe data center and/or to another data center, the scheduling tool 45may also account for the data center's average temperature, the locationof the physical systems and/or the current power consumption. Thescheduling tool 45 may review the application priority (as determined bythe SLA analysis tool 40), mean temperature of the data centerenvironmental parameters (as determined by the EA tool 35 and/orenvironmental sensors 50) and run time (as determined by the HA tool 30)to determine if a particular job may be a candidate and participant inan application job workload throttling to reduce energy needs and costs.

According to further aspects of the invention, the scheduling tool 45 isoperable to invoke application job workload throttling whenever possiblebased on maintaining respective SLAs of current and pending processingjobs. In additional embodiments, the scheduling tool 45 may invokeapplication job workload throttling based on real time job processingconditions (e.g., as determined by the EA tool 35). For example, if theEA tool 35 determines that a predetermined, user-configurable thresholdhas been met or exceeded (e.g., a temperature threshold, a humiditythreshold, a processing threshold and/or a power draw threshold, amongstother thresholds), the scheduling tool 45 may delay a lower prioritybatch job until the observed level drops below a predefined level.Additionally, for example, if the EA tool 35 determines a transactionjob (e.g., an OLAP/OLTP job) is above some critical level, e.g., a giventransaction processing job is running at 70% of capacity and ittypically only runs around 40% capacity (e.g., as determined by the HAtool 30), the scheduling tool 45 may delay a lower priority batch jobuntil the transaction job drops below a predefined level.

Online analytical processing, or OLAP, is an approach to quickly answermulti-dimensional analytical queries. The typical applications of OLAPare, for example, in business reporting for sales, marketing, managementreporting, business process management (BPM), budgeting and forecasting,financial reporting and similar areas. Online transaction processing, orOLTP, refers to a class of systems that facilitate and managetransaction-oriented applications, typically for data entry andretrieval transaction processing.

Exemplary Analysis

FIGS. 2 and 3 show exemplary depictions of a long-running applicationjob, e.g., that is about to be scheduled within the data center,processed at maximum capacity (as shown in FIG. 2) and at throttledcapacity (as shown in FIG. 3) in accordance with aspects of theinvention. Long-running jobs may be characterized, for example, by anyapplication job that may take longer than 30 minutes to complete basedon average run time. With this illustrative, non-limiting example, thejob characteristics are as follows:

-   -   Average Run Time: 5 hours (e.g., as determined by the HA tool        30)    -   SLA Target: 12 hours (e.g., as determined by the SLA analysis        tool 40)    -   Application Priority—“5” (e.g., as determined by the SLA        analysis tool 40)    -   Application Use Case: ETL (Extract, Transform, Load)    -   Systems Involved: (Extract—Server 1, Transform—Server 2,        Load—Server 3)

As shown in FIG. 2, if the job (e.g., application “A”) is executed atmaximum capacity, the job will complete in 5 hours. That is, two hourson server 1 operating at 100% capacity, one and one-half hours on server2 operating at 100% capacity and one and one-half hours on server 3operating at 100% capacity. As should be understood, processing the jobat maximum capacity generates excessive heat, which requires additionalcooling capacity, and thus, increased cooling costs.

As shown in FIG. 3, when the job (e.g., application “A”) is executed atthrottled capacity, in accordance with aspects of the invention, the jobwill complete in 11 hours. As can be observed, completing the job in 11hours will still be in compliance with the SLA, which requires jobcompletion within 12 hours. Thus, the scheduling tool 45 schedules theextract process to run for four hours on server 1 operating at 35%capacity, the transform process to run for three hours on server 2operating at 40% capacity and the load process to run for four hours onserver 3 operating at 20% capacity. As should be understood, byexecuting the job (e.g., application “A”) at throttled capacity,excessive heat, which requires additional cooling capacity, is avoided,thus decreasing cooling costs.

Based on the criteria outlined above, the job is executed at a reducedcapacity and will be finished in 11 hours versus the previous 5 hours.At the end of 11 hours, the application job is completed. Using thereduction of CPU and heat capacity, the application job has improved theenergy efficiencies of the data center and still meets the SLA targets.Calculating the energy improvements and monitoring the environmentalfactors of the data center, the application job may determine the energyimprovements and cost savings by throttling back the infrastructureresources. By time-shifting, throttling and/or relocating workload basedupon other computational workloads within a data center, the presentinvention is operable to smooth the heat characteristics within a givendata center.

Flow Diagrams

FIGS. 4 and 5 show exemplary flows for performing aspects of the presentinvention. The steps of FIGS. 4 and 5 may be implemented in theenvironment of FIG. 1, for example. The flow diagrams may equallyrepresent high-level block diagrams of the invention. The flowchartsand/or block diagrams in FIGS. 4 and 5 illustrate the architecture,functionality, and operation of possible implementations of systems,methods and computer program products according to various embodimentsof the present invention. In this regard, each block in the flowchartsor block diagrams may represent a module, segment, or portion of code,which comprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the blocks may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. Each block of each flowchart, andcombinations of the flowchart illustrations can be implemented byspecial purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions and/or software, as described above. Moreover, thesteps of the flow diagram may be implemented and executed from either aserver, in a client server relationship, or they may run on a userworkstation with operative information conveyed to the user workstation.In an embodiment, the software elements include firmware, residentsoftware, microcode, etc.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. The software and/or computer programproduct can be implemented in the environment of FIG. 1. For thepurposes of this description, a computer-usable or computer readablemedium can be any apparatus that can contain, store, communicate,propagate, or transport the program for use by or in connection with theinstruction execution system, apparatus, or device. The medium can be anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system (or apparatus or device) or a propagation medium.Examples of a computer-readable storage medium include a semiconductoror solid state memory, magnetic tape, a removable computer diskette, arandom access memory (RAM), a read-only memory (ROM), a rigid magneticdisk and an optical disk. Current examples of optical disks includecompact disk-read only memory (CD-ROM), compact disc-read/write (CD-R/W)and DVD.

FIG. 4 shows an exemplary flow 400 in accordance with aspects of theinvention. At step 405, the HA tool performs a historical analysis ofthe data center. In embodiments, the historical analysis may includereviews of historical application processing and/or determinations oftypical processing times for applications, amongst other analyses. Atstep 410, the EA tool performs an environmental analysis of the datacenter. In embodiments, the environmental analysis may include adetermination of current environmental conditions, including temperaturelevels, humidity levels, processing levels, hot spot locations, amongstother environmental conditions. At step 415, the EA tool reviews the jobscheduler data (e.g., accesses a database containing the job schedulerdata) to determine currently scheduled (e.g., upcoming) processing jobs.While steps 405, 410 and 415 have been shown as sequential steps, theinvention contemplates that these steps may occur concurrently andconstantly, updating, for example, in real time.

At step 420, the SLA analysis tool performs a service level agreement(SLA) analysis for one or more currently processing and/or upcomingjobs. In embodiments, the SLA analysis may include a determination ofSLAs for the one or more currently processing and/or upcoming jobs,which may include agreements pertaining to, for example, timerequirements, location requirements and/or certification requirements,amongst other agreements. At optional step 425 (as indicated by thedashed lines), the SLA analysis tool assigns a numerical value to one ormore currently processing and/or upcoming jobs. In embodiments, thenumerical value may be, for example, a throttle/delay numerical valueand/or a relocation numerical value. At step 430, the SLA analysis toolidentifies one or more candidate job processing applications for, e.g.,delay, slowing and/or relocation based on the SLA analysis.

At step 435, the scheduling tool modifies the job workload for a datacenter by modifying the schedule of one or more candidate applicationprocessing jobs, e.g., delay, slowing and/or relocating the job.Subsequent to step 435, the process returns to step 405.

FIG. 5 shows an exemplary flow 500 in accordance with aspects of theinvention. Steps 505-530 of FIG. 5 correspond to steps 405-430 of FIG. 4discussed above. At step 535, the scheduling tool determines whethercurrent conditions are suitable for an application workloadmodification. In embodiments, the scheduling tool may determine thatcurrent conditions are suitable for an application workloadmodification, for example, based on current data center environmentalconditions, e.g., excessive temperature, local hot spots, large powerdraws and/or large current processing loads, amongst other environmentalconditions (for example, levels beyond one or more user-configurablethresholds).

If, at step 535, the scheduling tool determines that the currentconditions are not suitable for an application job workloadmodification, the process returns to step 505. If, at step 535, thescheduling tool determines that the current conditions are suitable foran application job workload modification, at step 540, the schedulingtool modifies the job workload for a data center by modifying theschedule of one or more candidate application processing jobs, e.g.,delay, slowing and/or relocating the job. Subsequent to step 540, theprocess returns to step 505.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims, if applicable, areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprincipals of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated. Accordingly, while the invention has beendescribed in terms of embodiments, those of skill in the art willrecognize that the invention can be practiced with modifications and inthe spirit and scope of the appended claims.

What is claimed is:
 1. A computer implemented method comprising:performing a service level agreement (SLA) analysis for one or morecurrently processing or scheduled processing jobs of a data center usinga processor of a computer device the SLA analysis comprising:identifying one or more candidate processing jobs that qualify for aschedule modification from amongst the one or more currently processingor scheduled processing jobs using the processor of the computer device;determining a scheduled completion time for each of the one or morecandidate processing jobs according to an SLA; assigning first numericalvalues to each of the one or more candidate processing jobs based on apriority derived from the SLA, wherein the first numerical values areratios of an amount of time required for completing the one or morecandidate processing jobs to a time remaining until the scheduledcompletion time for each of the one or more candidate processing jobs;assigning second numerical values to each of the one or more candidateprocessing jobs based on whether each of the one or more candidateprocessing jobs can be processed in a different data center according tothe SLA; and ranking each of the one or more candidate processing jobsaccording to the first and second numerical values; performing anenvironmental analysis of the data center comprising determining anaverage temperature for regions of the data center; performing ahistorical analysis of the data center comprising analyzing an amount oftime used to process previous processing jobs, wherein the historicalanalysis includes estimating the amount of time required for completingthe one or more candidate processing jobs; and performing the schedulemodification for at least one of the one or more candidate processingjobs using the processor of the computer device, wherein the schedulemodification of the one or more candidate processing jobs is based onthe SLA analysis, the environmental analysis, and the historicalanalysis.
 2. The method of claim 1, wherein the schedule modificationcomprises at least one of: a delay of the one or more candidateprocessing jobs; a slowing of the one or more candidate processing jobs;and a relocation of the one or more candidate processing jobs from afirst location to a second location.
 3. The method of claim 2, whereinthe slowing of the one or more candidate processing jobs comprisesslowing one or more processor speeds of processors performing the one ormore candidate processing jobs.
 4. The method of claim 2, wherein therelocation of the one or more candidate processing jobs comprisesrelocating at least one of the one or more candidate processing jobs toat least one of: a cooler region of the data center; another datacenter; and a different climatic region having a different data center.5. The method of claim 1, wherein the historical analysis comprisesdetermining historical processing requirements for reoccurringprocessing jobs.
 6. The method of claim 1, wherein the environmentalanalysis comprises a determination of humidity levels of the data centerand air flow paths and at least one of: current environmental conditionsof the data center; temperature levels of the data center; processinglevels of the data center; hot spot locations within the data center;and current environmental conditions of one or more other data centers.7. The method of claim 1, further comprising analyzing job schedulerdata.
 8. The method of claim 1, wherein the performing the SLA) analysiscomprises determining one or more processing jobs, from the one or morecurrently processing or scheduled processing jobs, which can be subjectto the schedule modification without incurring an SLA violation.
 9. Themethod of claim 8, wherein the performing the schedule modification forat least one of the one or more candidate processing jobs is based on atleast one of: the first numerical values and the second numericalvalues; and the ranking.
 10. The method of claim 1, wherein the methodis implemented in a computer infrastructure having computer executablecode tangibly embodied on a computer readable storage medium havingprogramming instructions operable to perform the steps of claim
 1. 11.The method of claim 1, wherein a service provider at least one ofcreates, maintains, deploys and supports a computer infrastructure whichimplements the computer implemented method on hardware or a combinationof the hardware and software.
 12. The method of claim 11, wherein theservice provider at least one of creates, maintains, deploys andsupports the computer infrastructure on a subscription and/or fee basis.13. The method of claim 1, wherein the processor comprises at least oneof one or more processors and one or more independent computing devices.14. The method of claim 1, wherein the performing the schedulemodification comprises transforming data representative of a jobprocessing schedule to data representative of a modified job processingschedule.
 15. A system, comprising: a service level agreement (SLA)analysis tool operable to: perform a SLA analysis for one or morecurrently processing or scheduled processing jobs of a data center, theperforming the SLA analysis comprising: identify one or more candidateprocessing jobs for a schedule modification from amongst the one or morecurrently processing or scheduled processing jobs; determine a scheduledcompletion time for each of the one or more candidate processing jobsaccording to an SLA; assign first numerical values to each of the one ormore candidate processing jobs based on a priority of the SLA analysis,wherein the first numerical values are ratios of an amount of timerequired for completing each of the one or more candidate processingjobs to a time remaining until the scheduled completion time for each ofthe one or more candidate processing jobs; assign second numericalvalues to each of the one or more candidate processing jobs based onwhether each of the one or more candidate processing jobs can beprocessed in a different data center according to the SLA; and rank eachof the one or more candidate processing jobs according to the first andsecond numerical values; perform a historical analysis of the datacenter comprising analyzing an amount of time used to process previousprocessing jobs, wherein the historical analysis includes estimating theamount of time required for completing the one or more currentlyprocessing or scheduled processing jobs; and a scheduling tool operableto perform the schedule modification for at least one of the one or morecandidate processing jobs based on the SLA analysis and the historicalanalysis.
 16. The system of claim 15, further comprising: a historicalanalysis (HA) tool operable to perform the historical analysis of thedata center, wherein the historical analysis comprises determininghistorical processing requirements for reoccurring processing jobs; andan environmental analysis (EA) tool operable to perform an environmentalanalysis of the data center, wherein the environmental analysiscomprises a determination of at least one of: current environmentalconditions of the data center; temperature levels of the data center;humidity levels of the data center; processing levels of the datacenter; hot spot locations within the data center; and currentenvironmental conditions of one or more other data centers.
 17. Thesystem of claim 15, wherein the schedule modification comprises at leastone of: a delay of the one or more candidate processing jobs; a slowingof the one or more candidate processing jobs comprising slowing one ormore processor speeds of processors performing the one or more candidateprocessing jobs; and a relocation of the one or more candidateprocessing jobs to at least one of: a cooler region of the data center;another data center; and a different climatic region having a differentdata center.
 18. The system of claim 15, wherein the SLA analysiscomprises determining one or more processing jobs, from the one or morecurrently processing or scheduled processing jobs, which can be subjectto the schedule modification without incurring an SLA violation.
 19. Thesystem of claim 16, wherein at least one of the SLA analysis tool, thescheduling tool, the HA tool and the EA tool comprises hardware.
 20. Thesystem of claim 19, wherein the hardware comprises a server or acomputing device with one or more processors.
 21. A computer programproduct comprising a computer usable storage medium being hardware andhaving readable program code embodied in the storage medium, thecomputer program product being executed on a computing device andincludes at least one component operable to: perform a service levelagreement (SLA) analysis for one or more currently processing orscheduled processing jobs to determine one or more processing jobs, fromthe one or more currently processing or scheduled processing jobs, whichcan be subject to a schedule modification without incurring an SLAviolation, the performing the SLA analysis comprising: identify one ormore candidate processing jobs for the schedule modification fromamongst the one or more currently processing or scheduled processingjobs; determine a scheduled completion time for each of the one or morecandidate processing jobs according to an SLA; assign first numericalvalues to each of the one or more candidate processing jobs based on apriority of the SLA analysis, wherein the first numerical values areratios of an amount of time required for completing each of the one ormore candidate processing jobs to a time remaining until the scheduledcompletion time for each of the one or more candidate processing jobs;assign second numerical values to each of the one or more candidateprocessing jobs based on whether each of the one or more candidateprocessing jobs can be processed in a different data center according tothe SLA; and rank each of the one or more candidate processing jobsaccording to the first and second numerical values; perform a historicalanalysis of the data center comprising an amount of time used to processprevious processing jobs, wherein the historical analysis includesestimating the amount of time required for completing the one or morecandidate processing jobs; and perform the schedule modification for atleast one of the one or more candidate processing jobs based on the SLAanalysis and the historical analysis.
 22. The computer program productof claim 21, wherein the schedule modification comprises at least oneof: a delay of the one or more candidate processing jobs; a slowing ofthe one or more candidate processing jobs; and a relocation of the oneor more candidate processing jobs.
 23. A computer system for controllingheat dissipation through a service level agreement (SLA) analysis, thesystem comprising: a CPU, a computer readable memory and a computerreadable storage media; first program instructions to perform theservice level agreement (SLA) analysis for one or more currentlyprocessing or scheduled processing jobs of a data center; second programinstructions to identify one or more candidate processing jobs for aschedule modification from amongst the one or more currently processingor scheduled processing jobs; and third program instructions to performthe schedule modification for at least one of the one or more candidateprocessing jobs, wherein: the first, second and third programinstructions are stored on the computer readable storage media forexecution by the CPU via the computer readable memory; and the servicelevel agreement (SLA) analysis comprises: determining one or moreprocessing jobs, from the one or more currently processing or scheduledprocessing jobs, which can be subjected to the schedule modificationwithout incurring an SLA violation; determining a geographic location ofthe data center at which the one or more currently processing orscheduled processing jobs can be performed according to an SLA for theone or more currently processing or scheduled processing jobs; assigninga relocation numerical value to each of the one or more currentlyprocessing or scheduled processing jobs based on the geographic locationthat the one or more currently processing or scheduled processing jobscan be performed; ranking each of the one or more currently processingor scheduled processing jobs according to the relocation numericalvalue; determining that the one or more currently processing orscheduled processing jobs is eligible for relocation to a data center ata different geographic location based on the relocation numerical value;and relocating the one or more currently processing or scheduledprocessing jobs to the data center at the different geographic location.24. The computer system of claim 23, further comprising: a historicalanalysis (I-TA) tool operable to perform an historical analysis of thedata center, wherein the historical analysis comprises at least one of:determining historical processing requirements for reoccurringprocessing jobs; and estimating processing requirements for the one ormore currently processing or scheduled processing jobs; and anenvironmental analysis (EA) tool operable to perform an environmentalanalysis of the data center, wherein the environmental analysiscomprises a determination of at least one of: current environmentalconditions of the data center; temperature levels of the data center;humidity levels of the data center; processing levels of the datacenter; hot spot locations within the data center; and currentenvironmental conditions of one or more other data centers.
 25. Thecomputer system of claim 24, wherein the schedule modification comprisesat least one of: a delay of the one or more candidate processing jobs; aslowing of the one or more candidate processing jobs comprising slowingone or more processor speeds of processors performing the one or morecandidate processing jobs; and the relocation of the one or morecandidate processing jobs to at least one of: a cooler region of thedata center; another data center; and a different climatic region havinga different data center.